import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def 		snake_case__	(		lowercase=32							,				lowercase=10							,				lowercase=100							,				lowercase=1026							,				lowercase=True							,				lowercase="data/tokenized_stories_train_wikitext103.jbl"							,				lowercase="igf_context_pairs.jbl"							,				):
   set_seed(3					)
   # generate train_data and objective_set
   lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	generate_datasets(
       lowercase							,				lowercase							,				number=lowercase							,				min_len=1026							,				trim=lowercase					)
   # keeps model same across runs
   set_seed(4					)
   # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
   # can we train on GPU?
   lowerCAmelCase_:							int       =	torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)

   # load pretrained model
   lowerCAmelCase_:							int       =	load_gpta("gpt2"					).to(lowercase					)
   print("computing perplexity on objective set"					)
   lowerCAmelCase_:							Tuple       =	compute_perplexity(lowercase							,				lowercase							,				lowercase					).item()
   print("perplexity on objective set:"							,				lowercase					)

   # collect igf pairs and save to file demo.jbl
   collect_objective_set(lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					)

   # clean up, delete model and data we don't need anymore
   del model, train_data, objective_set
   torch.cuda.empty_cache()
def 		snake_case__	(		lowercase							,				lowercase=15							,				lowercase=128							,				lowercase=100							,				lowercase="igf_model.pt"							,				):
   set_seed(42					)

   # Load pre-trained model
   lowerCAmelCase_:							Optional[Any]       =	GPTaLMHeadModel.from_pretrained("gpt2"					)

   # Initialize secondary learner to use embedding weights of model
   lowerCAmelCase_:							Optional[int]       =	SecondaryLearner(lowercase					)

   # Train secondary learner
   lowerCAmelCase_:							Optional[int]       =	train_secondary_learner(
       lowercase							,				lowercase							,				max_epochs=lowercase							,				batch_size=lowercase							,				eval_freq=100							,				igf_model_path=lowercase							,				)

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase=32							,				lowercase=1000							,				lowercase=16							,				lowercase=1.0							,				lowercase=recopy_gpta							,				lowercase=None							,				lowercase=10							,				lowercase="gpt2_finetuned.pt"							,				):
   lowerCAmelCase_:							List[str]       =	torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)
   lowerCAmelCase_:							Dict       =	RandomSampler(lowercase					)
   lowerCAmelCase_:							List[Any]       =	DataLoader(lowercase							,				sampler=lowercase					)

   lowerCAmelCase_:							Any       =	max_steps // (len(lowercase					)) + 1
   lowerCAmelCase_:							List[str]       =	0
   lowerCAmelCase_:							Optional[int]       =	torch.zeros((1, context_len)							,				dtype=torch.long							,				device=lowercase					)
   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Any       =	recopy_model(lowercase							,				lowercase							,				lowercase					)

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(lowercase					)
      secondary_learner.eval()
   lowerCAmelCase_:							Union[str, Any]       =	[]
   lowerCAmelCase_:							str       =	0

   lowerCAmelCase_:							Optional[Any]       =	[]
   lowerCAmelCase_:							Tuple       =	[]

   # Compute the performance of the transformer model at the beginning
   lowerCAmelCase_:							str       =	compute_perplexity(lowercase							,				lowercase							,				lowercase					)
   test_perps.append(lowercase					)
   print("Test perplexity, step"							,				lowercase							,				":"							,				lowercase					)
   for epoch in range(int(lowercase					)					):
      for step, example in enumerate(lowercase					):
         torch.cuda.empty_cache()
         lowerCAmelCase_:							List[Any]       =	random.randint(0							,				example.size(2					) - context_len - 1					)
         lowerCAmelCase_:							List[Any]       =	example[0, 0, start : start + context_len]
         lm_optimizer.zero_grad()
         lowerCAmelCase_:							List[str]       =	model(lowercase							,				labels=lowercase					)
         lowerCAmelCase_:							Any       =	True

         if secondary_learner is not None:
            lowerCAmelCase_:							int       =	secondary_learner.forward(
                torch.tensor(lowercase							,				dtype=torch.long							,				device=lowercase					).unsqueeze(0					)					)[0].item()
            observed_qs.append(float(lowercase					)					)

            # Here we implement the simple non-constant threshold for the predicted IG(X) value
            # We will decay the selectivity of our secondary learner filter from
            # 1 standard deviation above average to 1 below average after 10 batches.

            if global_step == 10:
               lowerCAmelCase_:							Optional[Any]       =	-1
            if predicted_q < threshold:
               lowerCAmelCase_:							Any       =	False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()					)					)
            lowerCAmelCase_:							Optional[Any]       =	outputs[0]
            lm_loss.backward()
            examples += 1

         del outputs

         # Once the batch is filled with enough contexts, backprop on the batch.
         if examples == batch_size:
            torch.cuda.empty_cache()
            lowerCAmelCase_:							Any       =	0
            # Do LM backprop
            torch.nn.utils.clip_grad_norm_(model.parameters()							,				3.0					)
            lm_optimizer.step()
            lm_scheduler.step()  # Update learning rate schedule
            global_step += 1
            # Compute the performance of the transformer model at this batch
            if global_step % eval_interval == 0:
               lowerCAmelCase_:							Optional[int]       =	compute_perplexity(lowercase							,				lowercase							,				lowercase					)
               test_perps.append(lowercase					)

               print("Test perplexity, step"							,				lowercase							,				":"							,				lowercase					)
            # Break out of the loop after 60 batches
         if max_steps > 0 and global_step > 60:
            break
      if max_steps > 0 and global_step > 60:
         break

    # save finetuned transformer model
   torch.save(model.state_dict()							,				lowercase					)
   torch.cuda.empty_cache()
   # Do some cleaning up so we can reinitialize for the next run of this function
   del lm_optimizer
   del lm_scheduler
   return model
def 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"					)

   # Required parameters
   parser.add_argument(
       "--data_dir"							,				default=lowercase							,				type=lowercase							,				required=lowercase							,				help="The input data dir. Should contain data files for WikiText."							,				)
   parser.add_argument(
       "--model_name_or_path"							,				default=lowercase							,				type=lowercase							,				required=lowercase							,				help="Path to pretrained model or model identifier from huggingface.co/models"							,				)
   parser.add_argument(
       "--data_file"							,				type=lowercase							,				default=lowercase							,				help=(
           "A jbl file containing tokenized data which can be split as objective dataset, "
           "train_dataset and test_dataset."
       )							,				)

   parser.add_argument(
       "--igf_data_file"							,				type=lowercase							,				default=lowercase							,				help="A jbl file containing the context and information gain pairs to train secondary learner."							,				)

   parser.add_argument(
       "--output_dir"							,				default=lowercase							,				type=lowercase							,				required=lowercase							,				help="The output directory where the final fine-tuned model is stored."							,				)

   parser.add_argument(
       "--tokenizer_name"							,				default=lowercase							,				type=lowercase							,				help="Pretrained tokenizer name or path if not the same as model_name"							,				)
   parser.add_argument("--seed"							,				type=lowercase							,				default=lowercase							,				help="A seed for reproducible training."					)

   parser.add_argument(
       "--context_len"							,				default=32							,				type=lowercase							,				help=(
           "The maximum total input sequence length after tokenization. Sequences longer "
           "than this will be truncated, sequences shorter will be padded."
       )							,				)

   parser.add_argument(
       "--size_objective_set"							,				default=100							,				type=lowercase							,				help="number of articles that are long enough to be used as our objective set"							,				)
   parser.add_argument(
       "--eval_freq"							,				default=100							,				type=lowercase							,				help="secondary model evaluation is triggered at eval_freq"					)

   parser.add_argument("--max_steps"							,				default=1000							,				type=lowercase							,				help="To calculate training epochs"					)

   parser.add_argument(
       "--secondary_learner_batch_size"							,				default=128							,				type=lowercase							,				help="batch size of training data for secondary learner"							,				)

   parser.add_argument(
       "--batch_size"							,				default=16							,				type=lowercase							,				help="batch size of training data of language model(gpt2) "					)

   parser.add_argument(
       "--eval_interval"							,				default=10							,				type=lowercase							,				help=(
           "decay the selectivity of our secondary learner filter from"
           "1 standard deviation above average to 1 below average after 10 batches"
       )							,				)

   parser.add_argument(
       "--number"							,				default=100							,				type=lowercase							,				help="The number of examples split to be used as objective_set/test_data"					)

   parser.add_argument(
       "--min_len"							,				default=1026							,				type=lowercase							,				help="The minimum length of the article to be used as objective set"					)

   parser.add_argument(
       "--secondary_learner_max_epochs"							,				default=15							,				type=lowercase							,				help="number of epochs to train secondary learner"					)

   parser.add_argument("--trim"							,				default=lowercase							,				type=lowercase							,				help="truncate the example if it exceeds context length"					)

   parser.add_argument(
       "--threshold"							,				default=1.0							,				type=lowercase							,				help=(
           "The threshold value used by secondary learner to filter the train_data and allow only"
           " informative data as input to the model"
       )							,				)

   parser.add_argument("--finetuned_model_name"							,				default="gpt2_finetuned.pt"							,				type=lowercase							,				help="finetuned_model_name"					)

   parser.add_argument(
       "--recopy_model"							,				default=lowercase							,				type=lowercase							,				help="Reset the model to the original pretrained GPT-2 weights after each iteration"							,				)

   # function calls
   # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
   generate_n_pairs(
       context_len=32							,				max_steps=10							,				size_objective_set=100							,				min_len=1026							,				trim=lowercase							,				data_file="data/tokenized_stories_train_wikitext103.jbl"							,				igf_data_file="igf_context_pairs.jbl"							,				)

   # Load train data for secondary learner
   lowerCAmelCase_:							Optional[int]       =	joblib.load("data/IGF_values.jbl"					)

   # Train secondary learner
   lowerCAmelCase_:							int       =	training_secondary_learner(
       lowercase							,				secondary_learner_max_epochs=15							,				secondary_learner_batch_size=128							,				eval_freq=100							,				igf_model_path="igf_model.pt"							,				)

   # load pretrained gpt2 model
   lowerCAmelCase_:							int       =	GPTaLMHeadModel.from_pretrained("gpt2"					)
   set_seed(42					)

   # Generate train and test data to train and evaluate gpt2 model
   lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	generate_datasets(
       context_len=32							,				file="data/tokenized_stories_train_wikitext103.jbl"							,				number=100							,				min_len=1026							,				trim=lowercase					)

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       lowercase							,				lowercase							,				lowercase							,				context_len=32							,				max_steps=1000							,				batch_size=16							,				threshold=1.0							,				recopy_model=lowercase							,				secondary_learner=lowercase							,				eval_interval=10							,				finetuned_model_name="gpt2_finetuned.pt"							,				)


if __name__ == "__main__":
 main()
"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
